The personal and clinical utility of polygenic risk scores

Ali Torkamani, Nathan E. Wineinger and Eric J. Topol

TRANSLATIONAL GENETICS REVIEWS

Paper Discussion

Heritability in a population vs. individual disease risk

Although the total heritability explained by BRCA1 and BRCA2 variants is low [due to low prevalence of mutations], BRCA1 and BRCA2 testing can identify a subset of individuals whose absolute risk of disease is significantly higher than that of the average individual in the general population.

Disease risk:

• environmental exposures
• lifestyle factors
• genetic susceptibility ← heritability

Compute Polygenic risk scores (prs)

• select SNPs based on the significance of individual association test statistics
• weight SNPs according to corresponding estimated regression coefficients 

β: effect size in discovery sample from OLS (continuous trait) or logistic reg (binary trait; log(OR))

SNP_{ij}: # alleles (0,1,2) for SNP i of person j in target sample

• in PLINK: --score
• Slides from Matthew Keller
• Developing and evaluating polygenic risk prediction models for stratified disease prevention

PRS Utility​

depends on a fairly complex interplay between disease-specific and intervention-specific risks and benefits

• Therapeutic intervention: selection of interventions to treat or prevent disease

• Disease screening: decision to initiate and the interpretation of disease screens

• Life planning: the personal utility that PRSs can provide, even in the absence of preventive action

Therapeutic intervention

Coronary artery disease

• Highest quintile of genetic risk
  • ~30% increased risk of adverse coronary event
  • Statin therapy → ~45% reduction in 10-year risk of heart attack/CAD related death
• Intermediate quintiles: ~25% risk reduction
• PRS reclassifies ~12% intermediate risk → high risk

Individualized management of disease is central to the philosophy of precision medicine, with genetic factors often invoked for this strategy to personalize health care.

Disease screening

Breast cancer

• Age-based criterion: average risk + harms due to FP mammography
• Identify 16% of population to screen at 40 years old, 32% for delay screening

Colorectal cancer

• 42 years of highest PRS decile vs 52 years of lowest PRS decile

Prostate cancer

• Prioritize screening subgroup at high risk

Perspectives

Imperfect correlation with causal genetic factor(s) → Uncertainty in variant’s estimated effect size → Poor transferability → Inequities

Improve comprehensiveness and generalizability

• Capture uncertainty from measured and unmeasured factors of individual's risk estimates → distribution
• Dynamic model: demographics, lifestyle, clinical risk factors
• Integrate familial and polygenic risk
• Whole-genome prediction models

PRS model via ML and AI

• Requires large-scale data set
• Difficult to interpret

Discussion points

• description of common vs. rare risk variants
• result summary precision

https://slides.com/ttle/the-personal-and-clinical-utility-of-polygenic-risk-scores

Current PRS projects

• Polygenic scores for UK Biobank scale data
• Reconstructing the history of polygenic scores using coalescent trees
• Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Amit V. Khera, Mark Chaffin, Krishna G. Aragam, Mary E. Haas, Carolina Roselli, Seung Hoan Choi, Pradeep Natarajan, Eric S. Lander, Steven A. Lubitz, Patrick T. Ellinor &

Sekar Kathiresan

Methods

Statistical analysis (testing):

• best discriminative GPS: genotyped and imputed variants, Hail
• covariates: age, gender, 4 PCs of ancestry, genotyping array
• average predicted probability of disease from 100 groupings (percentile)

GPSs derivation:

• 31 candidate GPSs: 7 from LDPred, 24 from pruning and thresolding (p-value and LD-driven clumping, PLINK)
   

GPSs calculation:

• validation: 120,280 p1
• testing: 288,978 p2

genome-wide polgenic scores (GPS): MOTIVATION

• small GWAS, precision of estimated impact of variants
• algorithms
• validation datasets

Fig. 2: Risk for CAD according to GPS.

2a: Distribution of GPSCAD in population. For those in top 1% of CAD score, 11% had a heart attack by mean age 57 (vs 0.8% in lowest 1%).

2c: Y axis = PPV for CAD (by mean age 57). X axis = percentile bins of score; 100 bins: percentile of the score & prevalence of CAD within each bin plotted

Fig. 3: Risk gradient for disease according to the GPS percentile.

So...

• Compared to basing on rare monogenic mutations, PRS identifies larger fraction of the population who are at comparable or higher risk of having one of these common diseases.

• For those in top 8% of scores, 3-4-fold higher risk than all others.

• "Germline inherited component to any common disease can be captured by a single number that follows normal distribution."

• Integration with clinical and environmental factors